RRepoGEO

REPOGEO REPORT · LITE

datalayer/jupyter-mcp-server

Default branch main · commit 3f6ecb63 · scanned 6/28/2026, 5:37:53 AM

GitHub: 1,180 stars · 172 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface datalayer/jupyter-mcp-server, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's main description to highlight multi-cloud orchestration

    Why:

    CURRENT
    An MCP server developed for AI to connect and manage Jupyter Notebooks in real-time
    COPY-PASTE FIX
    A Model Context Protocol (MCP) server for orchestrating and managing Jupyter Notebooks across multi-cloud and multi-cluster environments, leveraging Kubernetes for real-time AI model development.
  • hightopics#2
    Add specific topics related to orchestration, multi-cloud, and Kubernetes

    Why:

    CURRENT
    ai, jupyter, mcp, mcp-server, tools
    COPY-PASTE FIX
    ai, jupyter, mcp, mcp-server, tools, orchestration, multi-cloud, multi-cluster, kubernetes, jupyterhub, real-time, server
  • mediumreadme#3
    Add a clear statement about the primary audience and use cases

    Why:

    COPY-PASTE FIX
    Jupyter MCP Server is designed for platform engineers, DevOps teams, and IT administrators who need to orchestrate and manage Jupyter Notebooks and AI applications consistently across diverse multi-cloud and multi-cluster environments.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface datalayer/jupyter-mcp-server
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Amazon SageMaker Studio
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Amazon SageMaker Studio · recommended 2×
  2. microsoft/vscode · recommended 2×
  3. jupyterlab/jupyterlab · recommended 1×
  4. jupyterlab/jupyter-ai · recommended 1×
  5. mlflow/mlflow · recommended 1×
  • CATEGORY QUERY
    How to programmatically manage Jupyter Notebooks in real-time for AI model development?
    you: not recommended
    AI recommended (in order):
    1. JupyterLab (jupyterlab/jupyterlab)
    2. Jupyter AI (jupyterlab/jupyter-ai)
    3. MLflow (mlflow/mlflow)
    4. Databricks Notebooks
    5. Google Colaboratory (Colab)
    6. Colab Enterprise
    7. Amazon SageMaker Studio
    8. SageMaker Notebooks
    9. Visual Studio Code (microsoft/vscode)
    10. Jupyter Extension (microsoft/vscode-jupyter)
    11. Live Share (microsoft/live-share)
    12. Ploomber (ploomber/ploomber)
    13. Deepnote

    AI recommended 13 alternatives but never named datalayer/jupyter-mcp-server. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What server solutions exist for connecting AI applications to Jupyter environments?
    you: not recommended
    AI recommended (in order):
    1. JupyterHub (jupyterhub/jupyterhub)
    2. Kubeflow Notebooks (kubeflow/notebooks)
    3. VS Code Remote Development extensions (microsoft/vscode)
    4. Google Cloud Vertex AI Workbench
    5. Amazon SageMaker Studio
    6. Azure Machine Learning Compute Instances
    7. BinderHub (jupyterhub/binderhub)

    AI recommended 7 alternatives but never named datalayer/jupyter-mcp-server. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of datalayer/jupyter-mcp-server?
    pass
    AI did not name datalayer/jupyter-mcp-server — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts datalayer/jupyter-mcp-server in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datalayer/jupyter-mcp-server explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo datalayer/jupyter-mcp-server solve, and who is the primary audience?
    pass
    AI named datalayer/jupyter-mcp-server explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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datalayer/jupyter-mcp-server — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite